caa a22 4500 605470200 CHVBK 20210128100326.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00500-014-1406-6 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1406-6 Dynamic deployment of virtual machines in cloud computing using multi-objective optimization [Elektronische Daten] [Bo Xu, Zhiping Peng, Fangxiong Xiao, Antonio Gates, Jian-Ping Yu] Cloud computing is regarded as the fifth utility service and is the next generation of computation. The computing resources can be dynamically allocated according to consumer requirements and preferences Virtual machine deployment has an important role in cloud computing, and aims to reduce turnaround times and improve resource use. In essence, the deployment of virtual machines is a multi-objective decision problem that must consider key factors. That is, we need to optimize the resource use and migration times. In this paper, we propose the multi-objective comprehensive evaluation model for the dynamic deployment of virtual machines. We then use an improved multi-objective particle swarm optimization (IMOPSO) to solve the problem. We have designed two simulation experiments using the CloudSim toolkit: the first experimental results show that on comparison of our improved algorithm with the traditional single-objective algorithms PSO and QPSO, our method is feasible and efficient; the second experimental results show that IMOPSO can search effectively, maintain population diversity, and quickly converge to the Pareto optimal solution without losing stability. The obtained Pareto optimal solution set has a better convergence and distribution than a comparative method. Springer-Verlag Berlin Heidelberg, 2014 Virtual machine deployment nationallicence Particle swarm optimization nationallicence Multi-objective optimization nationallicence Cloud computing nationallicence Xu Bo Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China aut Peng Zhiping Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China aut Xiao Fangxiong School of Software Engineering, South China University of Technology, 510006, Guangdong, China aut Gates Antonio Hawaii Pacific University, 96813, Honolulu, HI, USA aut Yu Jian-Ping Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China), College of Mathematics and Computer Science, Hunan Normal University, 410081, Hunan, China aut Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2265-2273 1432-7643 19:8<2265 2015 19 500 https://doi.org/10.1007/s00500-014-1406-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1406-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Xu Bo Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China aut NATIONALLICENCE 700 1- Peng Zhiping Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China aut NATIONALLICENCE 700 1- Xiao Fangxiong School of Software Engineering, South China University of Technology, 510006, Guangdong, China aut NATIONALLICENCE 700 1- Gates Antonio Hawaii Pacific University, 96813, Honolulu, HI, USA aut NATIONALLICENCE 700 1- Yu Jian-Ping Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China), College of Mathematics and Computer Science, Hunan Normal University, 410081, Hunan, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2265-2273 1432-7643 19:8<2265 2015 19 500